How do I convert this corpus of words from an online book into a term document matrix?

Question

Here is a snippet of my code:

library(gutenbergr)
library(tm)
Alice <- gutenberg_download(c(11))
Alice <- Corpus(VectorSource(Alice))
cleanAlice <- tm_map(Alice, removeWords, stopwords('english'))
cleanAlice <- tm_map(cleanAlice, removeWords, c('Alice'))
cleanAlice <- tm_map(cleanAlice, tolower)
cleanAlice <- tm_map(cleanAlice, removePunctuation)
cleanAlice <- tm_map(cleanAlice, stripWhitespace)
dtm1 <- TermDocumentMatrix(cleanAlice)
dtm1

But then I receive the following error:

<<TermDocumentMatrix (terms: 3271, documents: 2)>>
Non-/sparse entries: 3271/3271
Sparsity           : 50%
Error in nchar(Terms(x), type = "chars") : 
  invalid multibyte string, element 12

How should I deal with this? Should I convert the corpus into a plain text document first? Is there something wrong with the text format of the book?

Answer 1

Gutenbergr returns a data.frame, not a text vector. You just need to slightly adjust your code and it should work fine. Instead of VectorSource(Alice) you need VectorSource(Alice$text)

library(gutenbergr)
library(tm)

# don't overwrite your download when you are testing
Alice <- gutenberg_download(c(11))

# specify the column in the data.frame
Alice_corpus <- Corpus(VectorSource(Alice$text))
cleanAlice <- tm_map(Alice_corpus, removeWords, stopwords('english'))
cleanAlice <- tm_map(cleanAlice, removeWords, c('Alice'))
cleanAlice <- tm_map(cleanAlice, tolower)
cleanAlice <- tm_map(cleanAlice, removePunctuation)
cleanAlice <- tm_map(cleanAlice, stripWhitespace)
dtm1 <- TermDocumentMatrix(cleanAlice)
dtm1

<<TermDocumentMatrix (terms: 3293, documents: 3380)>>
Non-/sparse entries: 13649/11116691
Sparsity           : 100%
Maximal term length: 46
Weighting          : term frequency (tf)

P.S. you can ignore the warning messages in the code.